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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator
Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m3/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of CODeff and biogas yield, R2 values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.
Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator
Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m3/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of CODeff and biogas yield, R2 values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.
Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator
Front. Environ. Sci. Eng.
Li, Junlang (author) / Chen, Zhenguo (author) / Li, Xiaoyong (author) / Yi, Xiaohui (author) / Zhao, Yingzhong (author) / He, Xinzhong (author) / Huang, Zehua (author) / Hassaan, Mohamed A. (author) / El Nemr, Ahmed (author) / Huang, Mingzhi (author)
2023-06-01
Article (Journal)
Electronic Resource
English
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